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Orpheus 3b 0.1 Ft GGUF

Developed by Mungert
An ultra-low bit quantized model optimized based on the Llama-3-8B architecture, utilizing IQ-DynamicGate technology for adaptive 1-2 bit precision quantization, suitable for memory-constrained environments.
Downloads 1,427
Release Time : 4/3/2025

Model Overview

This model significantly improves the accuracy of low-bit quantization while maintaining extremely high memory efficiency through dynamic precision allocation and key component protection techniques, making it particularly suitable for edge devices and CPU inference.

Model Features

IQ-DynamicGate Ultra-low Bit Quantization
Uses a hierarchical strategy for adaptive 1-2 bit precision quantization, with the top 25% and bottom 25% layers using IQ4_XS and the middle 50% layers using IQ2_XXS/IQ3_S, significantly reducing error propagation.
Key Component Protection
Embedding and output layers are protected with Q5_K quantization, reducing error propagation by 38% compared to standard quantization.
Memory Efficiency Optimization
Validated on Llama-3-8B, the IQ1_M model reduces perplexity by 43.9% while increasing memory usage by only 0.3GB.

Model Capabilities

Text generation
Low-memory inference
Edge device deployment

Use Cases

Deployment in resource-constrained environments
Edge device AI assistant
Deploying a chatbot on memory-limited IoT devices
IQ2_S quantized version requires only 2.9GB memory with a perplexity of 9.02
CPU real-time inference
Running batch text generation on servers without GPUs
IQ4_K version achieves an inference speed of 246 tokens/s on CPU
Quantization technology research
Ultra-low bit algorithm validation
Serving as a test benchmark for new 1-3 bit quantization algorithms
Provides comprehensive comparison data for IQ1_S to IQ4_K quantization series
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